Yes, and the shift is becoming very noticeable across the industry.

Traditional data interviews focused heavily on syntax, manual problem solving, and tool-specific execution. Candidates were often evaluated on how quickly they could write SQL queries, clean datasets, or build dashboards from scratch.

Now that AI tools can assist with many of those tasks instantly, companies are starting to evaluate something deeper:
How candidates think in AI-assisted environments.

A strong data professional today is increasingly expected to:

• Frame problems clearly

• Understand business context

• Validate AI-generated outputs

• Identify flawed assumptions

• Communicate insights effectively

• Work alongside AI tools instead of depending blindly on them

Another major change is the rise of scenario-based interviews.

Many organizations now care more about:

• Decision-making ability

• Analytical reasoning

• System thinking

• Data reliability awareness

• Real-world business trade-offs

Because in production environments, the challenge is rarely just writing a query.
It’s understanding whether the output actually supports the right decision.

In many ways, data interviews are shifting from:
“Can you execute manually?”
to:
“Can you reason effectively in increasingly automated environments?”